where \(F\)  is the data in Fourier domain, \(u\)  and \(v\)  is the index of \(F\) , and \(x\)  and \(y\)  is the index of \(f\) . According to Eq.REF , we can observe that each value in Fourier domain depends on all values in Spatial domain. Hence, the size of the receptive field of a convolution layer in Fourier domain is the size of the entire image, so that the global information can be learned in Fourier domain. In addition, since each value in MR images contains a real part and an imaginary part, the previous methods [1]}, [2]} concatenate the real part and the imaginary part as the input. However, the features extracted by convolution are learned from the real part and the imaginary part together, which is not conducive to using Fourier transform on the features. Therefore, similar to some works [3]}, [4]}, we calculate the absolute value of MR images as the input and use real Fast Fourier transform (torch.fft.rfft) to replace Fast Fourier transform [5]}.
